Companies often store data in numerous tables, databases, and systems. Since companies often add, update, and delete data, duplicate data may be created in a plurality of tables, databases, and systems. Storage of such duplicate data consumes capacity of the database, which results in increasing maintenance cost and requiring a longer time for search.
It is common for the same data to be stored in numerous tables, databases, and systems. For example, if a system includes information for customers that send and receive packages, the system may store customer information such as a customer name, address, identification number, invoice number, and tracking information. However, the same customer information may be stored in more than one table with different column names and column data types. In addition, the same customer information may be stored in more than one database and system. Moreover, there may be missing column names.
One way to locate duplicate data is for company personnel to manually review all data in each table of a database. However, a manual review of numerous tables, databases, and systems could takes days, months, or years. As the number of tables, databases, and systems increase, manual review may become unworkable. In addition, using a computerized search methodology is inefficient. For example, there may be hundreds of thousands of tables that include customer information. These tables may include millions of fields and trillions of rows of data. The time needed to search for duplicate data may take thousands of years using a computer.
Accordingly, there is a need to reduce the time needed to search for duplicate data within tables, databases, and systems. To address these needs, a system is needed that may accurately and efficiently search and locate duplicate data.